In the field of automotive control and data utilization, and often in relation to autonomous and/or semi-autonomous vehicles, it is important to obtain naturalistic (e.g., real-world) data regarding vehicle events that can be used to develop systems for vehicle control. However, real-world vehicle events of interest are sparse and difficult to isolate from continuously collected real-world data, because the total real-world datasets from driving sessions often contain redundancies and/or large quantities of irrelevant or less-relevant information as related to vehicle events. Furthermore, the real-world data is typically collected at the “edge” of the computational network (e.g., in the vehicle during driving), where computing power and storage is limited (e.g., by physical constraints, thermal constraints, power constraints, etc.), and often substantially processed and/or stored in the cloud (e.g., at a remote computing system), which is associated with communication latency and bandwidth costs. However, it is also desirable to meet accuracy requirements at the computing edge, and latency/cost requirements associated with cloud computing.
Thus, there is a need in the automotive field to create a new and useful method for distributed data analysis. This invention provides such a new and useful method.